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from caffe2.python import schema
from caffe2.python.layers.layers import (
IdList,
ModelLayer,
)
# Model layer for implementing probabilistic replacement of individual elements in
# IdLists. Takes probabilities for train, eval and predict nets as input, as
# well as the replacement value when dropout happens. For features we may have
# available to us in train net but not in predict net, we'd set dropout
# probability for predict net to be 1.0 and set the feature to the replacement
# value given here. This way, the value is tied to the particular model and not
# to any specific logic in feature processing in serving.
# Consider the following example where X is the values in the IdList and Lengths
# is the number of values corresponding to each example.
# X: [1, 2, 3, 4, 5]
# Lengths: [2, 3]
# This IdList contains 2 IdList features of lengths 2, 3. Let's assume we used a
# ratio of 0.5 and ended up dropping out 2nd item in 2nd IdList feature, and used a
# replacement value of -1. We will end up with the following IdList.
# Y: [1, 2, 3, -1, 5]
# OutputLengths: [2, 3]
# where the 2nd item in 2nd IdList feature [4] was replaced with [-1].
class SparseItemwiseDropoutWithReplacement(ModelLayer):
def __init__(
self,
model,
input_record,
dropout_prob_train,
dropout_prob_eval,
dropout_prob_predict,
replacement_value,
name='sparse_itemwise_dropout',
**kwargs):
super(SparseItemwiseDropoutWithReplacement, self).__init__(model, name, input_record, **kwargs)
assert schema.equal_schemas(input_record, IdList), "Incorrect input type"
self.dropout_prob_train = float(dropout_prob_train)
self.dropout_prob_eval = float(dropout_prob_eval)
self.dropout_prob_predict = float(dropout_prob_predict)
self.replacement_value = int(replacement_value)
assert (self.dropout_prob_train >= 0 and
self.dropout_prob_train <= 1.0), \
"Expected 0 <= dropout_prob_train <= 1, but got %s" \
% self.dropout_prob_train
assert (self.dropout_prob_eval >= 0 and
self.dropout_prob_eval <= 1.0), \
"Expected 0 <= dropout_prob_eval <= 1, but got %s" \
% dropout_prob_eval
assert (self.dropout_prob_predict >= 0 and
self.dropout_prob_predict <= 1.0), \
"Expected 0 <= dropout_prob_predict <= 1, but got %s" \
% dropout_prob_predict
assert(self.dropout_prob_train > 0 or
self.dropout_prob_eval > 0 or
self.dropout_prob_predict > 0), \
"Ratios all set to 0.0 for train, eval and predict"
self.output_schema = schema.NewRecord(model.net, IdList)
if input_record.lengths.metadata:
self.output_schema.lengths.set_metadata(
input_record.lengths.metadata)
if input_record.items.metadata:
self.output_schema.items.set_metadata(
input_record.items.metadata)
def _add_ops(self, net, ratio):
input_values_blob = self.input_record.items()
input_lengths_blob = self.input_record.lengths()
output_lengths_blob = self.output_schema.lengths()
output_values_blob = self.output_schema.items()
net.SparseItemwiseDropoutWithReplacement(
[
input_values_blob,
input_lengths_blob
],
[
output_values_blob,
output_lengths_blob
],
ratio=ratio,
replacement_value=self.replacement_value
)
def add_train_ops(self, net):
self._add_ops(net, self.dropout_prob_train)
def add_eval_ops(self, net):
self._add_ops(net, self.dropout_prob_eval)
def add_ops(self, net):
self._add_ops(net, self.dropout_prob_predict)
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